Neural network-based modeling of solid oxide fuel cells for marine applications

Abstract

As the solid oxide fuel cell (SOFC) experimental test is still quite cost-effective and time-consuming, there is a growing need for developing effective simulation tools to reduce the time and cost of the marine SOFC performance test and optimization. The present paper is aimed to study the modeling and simulation of marine solid oxide fuel cells by artificial intelligence method. A neural network based on a particle swarm optimization algorithm is used to establish a marine solid oxide fuel cell model for voltage/current characteristic analysis. The model is also compared with BP neural network and Hopfield neural network methods. The simulation results compared with experimental data show that the effectivity of the particle swarm optimization neural network algorithm is best, which can accurately predict the voltage/current characteristic curves of a SOFC under different fuel flow-air volume ratios. The model study can provide support for SOFC performance characteristics analyses and has significant potential in SOFC optimization applications.